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I am using the seas function to run a X13 Arima on a time series. But when I call the seasonal, trend and irregular component I see that the seas function is providing me a multiplicative decompositio…
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### Story
As developer I want to be able to get an AVG over time when using a time series database in order to avoid biases introduces by irregular data capture intervals.
### Requirement
- […
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- Download _SP&500_ data.
- _Clean_ dataset for analysis.
- Convert from xlsx to csv or txt.
- Change decimal separator from commas to points.
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As a first step into time series forecasting, add an R wrapper into deep learning to forecast.
A common use of neural networks in forecasting is to take the prior period values as columns and run …
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**Describe the bug**
When performing a forecast of a specific time series (data provided below) the method `make_reduction` from `compose` module returns `NaN` value, even using different scikit-le…
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Hi, Thanks a lot for the brilliant repo!
I ran the experiment in 5.3 experiments (Learning dynamical systems from irregularly-sampled time series Experiments) and the results are strange.
I ran b…
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There are multiple issues with [Rule PD101](https://docs.astral.sh/ruff/rules/pandas-nunique-constant-series-check/):
1. `(s[0] == s).all()` can actually be slower than `s.nunique()
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I notice that increasingly we end up having to implement algorithms for a sequence and then the same algorithm for a time step. In theory a time series is something that is based on time, not on index…
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- Clash - This is a clash and a recommended harmony can be (Short and simple)
- Too many warnings are not user friendly
- Warnings should point out exact problem like months have irregular number of…
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Hello,
I am trying to extract the real time-series data for an analysis separate from the model produced by bfast/bfastmonitor (basically I want to use only the real data to eliminate false positi…